Comparison of bias adjustment methods in meta-analysis suggests that quality effects modeling may have less limitations than other approaches
Date
2020
Authors
Stone, Jennifer
Glass, Kathryn
Munn, Zachary
Tugwell, Peter
Doi, Suhail A R
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
Background
The quality of primary research is commonly assessed before inclusion in meta-analyses. Findings are discussed in the context of the quality appraisal by categorizing studies according to risk of bias. The impact of appraised risk of bias on study outcomes is typically judged by the reader; however, several methods have been developed to quantify this risk of bias assessment and incorporate it into the pooled results of meta-analysis, a process known as bias adjustment. The advantages, potential limitations, and applicability of these methods are not well defined.
Study Design and Setting
Comparative evaluation of the applicability of the various methods and their limitations are discussed using two examples from the literature. These methods include weighting, stratification, regression, use of empirically based prior distributions, and elicitation by experts.
Results
Use of the two examples from the literature suggest that all methods provide similar adjustment. Methods differed mainly in applicability and limitations.
Conclusion
Bias adjustment is a feasible process in meta-analysis with several strategies currently available. Quality effects modelling was found to be easily implementable with fewer limitations in comparison to other methods.
Description
Keywords
Citation
Collections
Source
Journal of Clinical Epidemiology
Type
Journal article
Book Title
Entity type
Access Statement
License Rights
Restricted until
2037-12-31
Downloads
File
Description